PD37-11 MULTI-CENTERED ASSESSMENT OF CLINICAL OUTCOMES AND FACTORS ASSOCIATED WITH FAILURE OF THE ADJUSTABLE TRANSOBTURATOR MALE SYSTEM (ATOMS)
Bibliographic record
Abstract
METHODS: AUS and male sling procedures performed between 2003 and 2015 in both inpatient and outpatient ambulatory setting in New York State were extracted from the Statewide Planning and Research Cooperative System (SPARCS) database utilizing CPT and ICD-9/10 procedure codes.Spearman correlation analysis was performed to assess trends.RESULTS: A total of 1830 male sling placements and 1481 AUS insertions were identified.AUS placement increased steadily from 45 cases in 2003 to 221 cases in 2015 (P <0.001) (figure 1A).Male sling placement trended upwards from 51 cases in 2003 to 134 in 2015 (p[0.049).It surpassed AUS placement from 2008-2013 but slowly decreased by 40% from 228 cases in 2009 to 134 cases in 2015.AUS removal/revision trended upwards during the study period (p<0.001)(figure 1B).Interestingly, despite an overall increase in sling placement, sling revision/removal remained relatively stable.CONCLUSIONS: In New York State, the utilization of both AUS and male urethral sling has increased over the past decade.AUS removal/revision trended upwards during this time period, whereas incidence of male sling revision/removal remained relatively low and stable.Future studies are warranted to investigate physician and patient factors influencing the trends of surgical management of male SUI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".